This is a Guest Article by Mr. Neelesh Kripalani, Chief Technology Officer, Clover Infotech
Today, as the world moves towards a complete digital transformation, data has taken centre stage in an organization’s journey. It is also considered to be a core component for consistent growth.
Organizations that have relevant insights about their customers can have an edge over their competitors in terms of customer acquisition, engagement, and retention.
Where can organizations find the data? And how can they analyse it?
This is one of the biggest challenges that organizations face today. A lot of companies already have the data that is critical for informed decision-making. According to Forrester, between 60 percent and 73 percent of all data within an enterprise remains unused for analytics. It is a simple task to gather structured data and perform analytics on it. However, this is just the beginning. The main challenge lies in having to go through the unstructured data and deliver personalized offerings and experiences to the customer.
Today, many organizations invest time and money in misjudged targeting or umbrella customer engagement with generic offerings and messaging that leads to a lower conversion rate. This can easily be optimized by understanding your customers and whether they are the right target audience for your campaign or not.
Every business needs to understand its data first. Unstructured data is nothing but data that does not have a recognizable structure and cannot easily fit into a structured sheet. A few examples of unstructured data are emails, webpages, social media profiles, open-ended surveys, voice commands, etc. Undoubtedly, recognizing unstructured data is difficult. However, with the right kind of tools, organizations can analyse unstructured data. Advancements in AI and ML have offered us many new ways in which this data can be recognized, cleaned, and simplified.
For example, today a bank can go through the digital footprints of their customers and understand their purchase patterns, spending behaviour, travel patterns etc. to create a customized loan or card offering.
Enterprises should also combine and incorporate data silos to realise the complete potential of unstructured data and create a scalable data lake. They can leverage and derive extensive business value by incorporating systems to store and analyse data from a variety of sources and share it with decision-makers.
Here are three steps that will help you to grow your business using Unstructured Data
- Identifying the sources of data
It is important to first identify the data points around your customers that are essential for product development and marketing. Make sure that you only gather the relevant data and filter out the rest. You should also try to save on data storage costs by optimizing the data. Information from internet-enabled devices such as smartphones, apps, browsers, etc. can be good sources. Apart from this, your data source can also include information from online reviews, customer feedback forms and more.
- Creating a specific end goal
There is a huge volume of data out there, and starting to analyse it without a roadmap will only lead to more confusion. As data extraction has a cost, it is very important to chalk out a clear plan on what you want to achieve from this data. Objectives can be as simple as figuring out how the public reacted to a marketing campaign, or how a brand is perceived by its target audience. Having a clear end-goal in mind will significantly help you to analyse this data.
- Developing data models that support end-objectives
Once you have all the data, it is very important to clean and simplify it to allow business intelligence tools to structure it and create reports that can facilitate business decisions. Further, the analytics teams should create a data extraction architecture and data consumption processes that work seamlessly, is time-efficient and generate quicker results. The use of AI and ML will likely increase the initial investment but will result in significantly better returns due to their quicker data modelling and pattern recognition capabilities.
Today, as 95% of the data generated is unstructured, it is very important that organizations find ways to extract, analyse, and make full use of this data to facilitate smarter business decisions.
